21.06.2005
A biometric system can be viewed as a pattern recognition system
consisting of three main modules: the sensor module, the feature
extraction module and the feature matching module. The design of
such a system is studied in the context of many commonly used
biometric modalities - fingerprint, face, speech, hand, iris.
Various algorithms that have been developed for each of these
modalities will be presented.

16.05.2006
A neural network is an interconnected group of biological neurons. In modern usage the term can also refer to artificial neural networks,
which are constituted of artificial neurons. Thus the term 'Neural Network' specifies two distinct concepts:

- A biological neural network is a plexus of connected or functionally related neurons in the peripheral nervous system or
the central nervous system.

- In the field of neuroscience, it most often refers to a group of neurons from a nervous system
that are suited for laboratory analysis.

Artificial neural networks were designed to model some properties of biological neural networks, though most of the applications
are of technical nature as opposed to cognitive models. Neural networks are made of units that are often assumed to be simple in the sense that their state can be described
by single numbers, their "activation" values. Each unit generates an output signal based on its activation. Units are connected
to each other very specifically, each connection having an individual "weight" (again described by a single number).
Each unit sends its output value to all other units to which they have an outgoing connection. Through these connections,
the output of one unit can influence the activations of other units. The unit receiving the connections calculates
its activation by taking a weighted sum of the input signals (i.e. it multiplies each input signal with the weight
that corresponds to that connection and adds these products). The output is determined by the activation function based on this
activation (e.g. the unit generates output or "fires" if the activation is above a threshold value). Networks learn by changing
the weights of the connections. In general, a neural network is composed of a group or groups of physically connected or functionally
associated neurons. A single neuron can be connected to many other neurons and the total number of neurons and connections in a network
can be extremely large. Connections, called synapses are usually formed from axons to dendrites, though dendrodentritic microcircuits
and other connections are possible. Apart from the electrical signalling, there are other forms of signaling that arise from neurotransmitter
diffusion, which have an effect on electrical signaling. Thus, like other biological networks, neural networks are extremely complex.

While a detailed description of neural systems seems currently unattainable, progress is made towards a better understanding of
basic mechanisms. Artificial intelligence and cognitive modeling try to simulate some properties of neural networks.
While similar in their techniques, the former has the aim of solving particular tasks, while the latter aims to build mathematical
models of biological neural systems. In the artificial intelligence field, artificial neural networks have been applied
successfully to speech recognition, image analysis and adaptive control, in order to construct software agents
(in computer and video games) or autonomous robots. Most of the currently employed artificial neural networks for
artificial intelligence are based on statistical estimation, optimisation and control theory. The cognitive modelling
field is the physical or mathematical modelling of the behaviour of neural systems; ranging from the individual neural level
(e.g. modelling the spike response curves of neurons to a stimulus), through the neural cluster level
(e.g. modelling the release and effects of dopamine in the basal ganglia) to the complete organism (e.g. behavioural
modelling of the organism's response to stimuli).

11.06.2007
Genetic algorithms constitute a class of search, adaptation, and optimization
techniques based on the principles of natural evolution. Genetic algorithms
were developed by Holland. Other evolutionary algorithms include
evolution strategies, evolutionary programming, classifier systems, and
genetic programming. An evolutionary algorithm maintains a population of
solution candidates and evaluates the quality of each solution candidate
according to a problem-specific fitness function, which defines the
environment for the evolution. New solution candidates are created by
selecting relatively fit members of the population and recombining them
through various operators. Specific evolutionary algorithms di¤er in the
representation of solutions, the selection mechanism, and the details of the
recombination operators. In a genetic algorithm, solution candidates are
represented as character strings from a given (often binary) alphabet. In a
particular problem, a mapping between these genetic structures and the
original solution space has to be developed, and a fitness function has to be
defined. The fitness function measures the quality of the solution
corresponding to a genetic structure. In an optimization problem, the fitness
function simply computes the value of the objective function. In other
problems, fitness could be determined by a coevolutionary environment
consisting of other genetic structures. For instance, one could study the
equilibrium properties of game-theoretic problems whereby a population of
strategies evolves with the fitness of each strategy defined as the average
payoff against the other members of the population. A genetic algorithm starts
with a population of randomly generated solution candidates. The next
generation is created by recombining promising candidates. The
recombination involves two parents chosen at random from the population,
with the selection probabilities biased in favor of the relatively fit candidates.
The parents are recombined through a crossover operator, which splits the
two genetic structures apart at randomly chosen locations, and joins a piece
from each parent to create an offspring (as a safeguard against the loss of
genetic diversity, random mutations are occasionally introduced into the
offspring). The algorithm evaluates the fitness of the offspring and replaces
one of the relatively unfit members of the population. New genetic structures
are produced until the generation is completed. Successive generations are
created in the same manner until a well-defined termination criterion is
satisfied. The final population provides a collection of solution candidates,
one or more of which can be applied to the original problem. Even though evolutionary algorithms
are not guaranteed to find the global optimum, they can find an acceptable
solution relatively quickly in a wide range of problems.

Evolutionary
algorithms have been applied to a large number of problems in engineering,
computer science, cognitive science, economics, management science, and
other fields. The number of practical applications has been rising steadily,
especially since the late 1980s. Typical business applications involve
production planning, job-shop scheduling, and other difficult combinatorial
problems. Genetic algorithms have also been applied to theoretical questions
in economic markets, to time series forecasting, and to econometric
estimation. String-based genetic algorithms have been applied to finding
market-timing strategies based on fundamental data for stock and bond
markets.

Scilab
- Scilab is a scientific software package for numerical computations providing a powerful open computing environment for
engineering and scientific applications. Developed since 1990 by researchers from INRIA and ENPC, it is now maintained
and developed by Scilab Consortium since its creation in May 2003.

Octave
- Octave is a high-level language, primarily intended for numerical
computations. It provides a convenient command line interface for
solving linear and nonlinear problems numerically, and for performing
other numerical experiments using a language that is mostly compatible
with Matlab. It may also be used as a batch-oriented language.

Python
- Python is a dynamic object-oriented programming language that can be used for many kinds of software development.
It offers strong support for integration with other languages and tools, comes with extensive standard libraries, and can
be learned in a few days. Many Python programmers report substantial productivity gains and feel the language encourages
the development of higher quality, more maintainable code.

Stock Price Trend Forecasting
An emerging trading market is represented by binary options. Binary options are a convenient way of investments as they don’t
require a trader to forecast actual quotes.

New - Speaker Verification System

Text-Independent Speaker Authentication
There are two major applications of speaker recognition technologies and methodologies. If the speaker claims to be of a certain identity and the voice is
used to verify this claim, this is called verification or authentication.

New - Java Face Recognition

Java-based Biometric Authentication System
Face recognition is essential in many applications, including mugshot matching, surveillance,
access control and personal identification, and forensic and law enforcement applications.

New - Software References

Papers and lectures
A list of papers that included Advanced Source Code .Com in the references section. If you have written a paper where our software is cited in the references list please email us and your work will be published at our web site.

Face Recognition Based on Fractional Gaussian Derivatives
Local photometric descriptors computed for interest regions have proven to be very successful in applications such as wide baseline matching, object
recognition, texture recognition, image retrieval, robot localization, video data mining, building panoramas, and recognition of object categories.

New - Speaker Recognition System

Source
code for speaker recognitionSpeaker recognition is the process of automatically recognizing who
is speaking on the basis of individual information included in speech
waves.

New - Speech Recognition System

Source
code for isolated words recognitionSpeech recognition technology is used
more and more for telephone applications like travel booking and
information, financial account information, customer service
call routing, and directory assistance. Using constrained
grammar recognition, such applications can achieve remarkably
high accuracy.